TY - GEN
T1 - A survey of uncertainty handling in frequent subgraph mining algorithms
AU - Moussaoui, Mohamed
AU - Zaghdoud, Montaceur
AU - Akaichi, Jalel
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2016/7/7
Y1 - 2016/7/7
N2 - Frequent subgraph mining is useful in most knowledge discovery tasks such as classification, clustering and indexing. Many algorithms and methods have been developed to mine frequent subgraphs. To have an understanding of several mining frequent subgraph algorithms, it is advantageous to establish a common framework for their study. In this paper, we propose a comparative study of several approaches by focusing on the intrinsic characteristics of these algorithms. A set of existing approaches in literature are reviewed and categorized according to the certainty nature of input which can be exact or uncertain graphs.
AB - Frequent subgraph mining is useful in most knowledge discovery tasks such as classification, clustering and indexing. Many algorithms and methods have been developed to mine frequent subgraphs. To have an understanding of several mining frequent subgraph algorithms, it is advantageous to establish a common framework for their study. In this paper, we propose a comparative study of several approaches by focusing on the intrinsic characteristics of these algorithms. A set of existing approaches in literature are reviewed and categorized according to the certainty nature of input which can be exact or uncertain graphs.
UR - https://www.scopus.com/pages/publications/84980398093
U2 - 10.1109/AICCSA.2015.7507186
DO - 10.1109/AICCSA.2015.7507186
M3 - Conference contribution
AN - SCOPUS:84980398093
T3 - Proceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA
BT - 2015 IEEE/ACS 12th International Conference of Computer Systems and Applications, AICCSA 2015
PB - IEEE Computer Society
T2 - 12th IEEE/ACS International Conference of Computer Systems and Applications, AICCSA 2015
Y2 - 17 November 2015 through 20 November 2015
ER -